README.md

---
license: cc-by-4.0
tags:
  - satellite
  - sentinel-2
  - remote-sensing
  - ssl
---

# S2-100K Preprocessed

Derived from [torchgeo/s2-100k](https://huggingface.co/datasets/torchgeo/s2-100k).

## What changed

The original dataset stores patches as uint16 GeoTIFF files inside plain tar
archives with no compression.  This version converts every patch to a
blosc2/zstd-compressed float32 array for faster I/O in training pipelines.

**No season selection is performed.** Each patch in the source dataset is a
single Sentinel-2 L2A acquisition (no temporal dimension), so every patch is
included as-is.

## Source statistics

| Property | Value |
|----------|-------|
| Total patches | 100,000 |
| Shards | 100 (1,000 patches each) |
| Spatial size | 256 × 256 px (resampled to 10 m/px) |
| Spectral bands | 12 (B01–B09, B11, B12; no B10) |
| DN scale | L2A reflectance × 10000 (uint16 in source) |

**Band order** (index 0–11):
B01, B02, B03, B04, B05, B06, B07, B08, B08A, B09, B11, B12

## Format

WebDataset `.tar` shards under `train/`.
Each sample contains two files:

| File | Description |
|------|-------------|
| `{patch_id}.bands.b2` | blosc2/zstd-compressed `[12, 256, 256]` **float32** array |
| `{patch_id}.meta.json` | {"lon":…,"lat":…,"fn":…,"shard":…,"patch_idx":…} |

`patch_id` format: `s2100k_{shard:05d}_{patch_idx:05d}`

`patch_idx` is the **shard-local** 0-based index (0–999), matching the
`patch_idx` column in the source `metadata.parquet`.
e.g. `s2100k_00003_00042` = shard 3, the 43rd patch in that shard (0-indexed).

## Loading a sample

```python
import blosc2, numpy as np, json, tarfile

N_CHANNELS, H, W = 12, 256, 256

with tarfile.open('s2100k_preprocessed_shard_00000.tar') as tf:
    members = {m.name: m for m in tf.getmembers()}
    patch_id = 's2100k_00000_00000'
    raw  = blosc2.decompress(tf.extractfile(members[f'{patch_id}.bands.b2']).read())
    arr  = np.frombuffer(raw, dtype=np.float32).reshape(N_CHANNELS, H, W)
    meta = json.loads(tf.extractfile(members[f'{patch_id}.meta.json']).read())
    print(arr.shape, arr.dtype, meta)
```